In this tutorial, we offer an overview of different machine learning (ML) methodologies for turbulence modeling. The main focus is on RANS and LES but other reduced-order models are also discussed. First, the distinction between models and methods is highlighted. The different ML approaches are introduced according to three classifications: i) the level of modeling form of closure terms, ii) machine learning methodology and, iii) the neural network architecture. A selected number of works are used to illustrate the ML approaches. Finally, we address the question: What can we do with machine-learning models for turbulence that we couldn’t do before?